Julio C. Urenda

Orcid: 0000-0002-3220-824X

According to our database1, Julio C. Urenda authored at least 15 papers between 2008 and 2023.

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Bibliography

2023
Dimension Compactification Naturally Follows from First Principles.
Proceedings of the Decision Making Under Uncertainty and Constraints - A Why-Book, 2023

Why Menzerath's Law?
Proceedings of the Uncertainty, Constraints, and Decision Making, 2023

Why Homogeneous Membranes Lead to Optimal Water Desalination: A Possible Explanation.
Proceedings of the Decision Making Under Uncertainty and Constraints - A Why-Book, 2023

Fuzzy Techniques, Laplace Indeterminacy Principle, and Maximum Entropy Approach Explain Lindy Effect and Help Avoid Meaningless Infinities in Physics.
Proceedings of the Decision Making Under Uncertainty and Constraints - A Why-Book, 2023

2022
Why 1/(1+d) Is an Effective Distance-Based Similarity Measure: Two Explanations.
Proceedings of the IEEE 11th International Conference on Intelligent Systems, 2022

Algebraic Approach to Data Processing - Techniques and Applications
115, Springer, ISBN: 978-3-031-16779-9, 2022

2021
Mexican Folk Arithmetic Algorithm Makes Perfect Sense.
Proceedings of the Explainable AI and Other Applications of Fuzzy Techniques, 2021

2020
Why Squashing Functions in Multi-Layer Neural Networks.
Proceedings of the 2020 IEEE International Conference on Systems, Man, and Cybernetics, 2020

How Mathematics and Computing Can Help Fight the Pandemic: Two Pedagogical Examples.
Proceedings of the Fuzzy Information Processing 2020, 2020

Why a Classification Based on Linear Approximation to Dynamical Systems Often Works Well in Nonlinear Cases.
Proceedings of the Fuzzy Information Processing 2020, 2020

How User Ratings Change with Time: Theoretical Explanation of an Empirical Formula.
Proceedings of the Fuzzy Information Processing 2020, 2020

Natural Invariance Explains Empirical Success of Specific Membership Functions, Hedge Operations, and Negation Operations.
Proceedings of the Fuzzy Information Processing 2020, 2020

Which Distributions (or Families of Distributions) Best Represent Interval Uncertainty: Case of Permutation-Invariant Criteria.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020

Why Spiking Neural Networks Are Efficient: A Theorem.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems, 2020

2008
How to reconcile physical theories with the idea of free will: From analysis of a simple model to interval and fuzzy approaches.
Proceedings of the FUZZ-IEEE 2008, 2008


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